A Systematic Review of Machine Learning Techniques and Applications in Soil Improvement Using Green Materials

Ahmed Hassan Saad, Haslinda Nahazanan (), Badronnisa Yusuf, Siti Fauziah Toha, Ahmed Alnuaim, Ahmed El-Mouchi, Mohamed Elseknidy and Angham Ali Mohammed
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Ahmed Hassan Saad: Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
Haslinda Nahazanan: Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
Badronnisa Yusuf: Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
Siti Fauziah Toha: Department of Mechatronics, Faculty of Engineering, International Islamic University Malaysia (IIUM), Kuala Lumpur 53100, Malaysia
Ahmed Alnuaim: College of Engineering, Civil Engineering Department, King Saud University, Riyadh 11421, Saudi Arabia
Ahmed El-Mouchi: School of Engineering, Faculty of Applied Science, The University of British Columbia, Okanagan Campus, 3333 University Way, Kelowna, BC V1V 1V7, Canada
Mohamed Elseknidy: Department of Chemical and Environmental Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
Angham Ali Mohammed: Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia

Sustainability, 2023, vol. 15, issue 12, 1-37

Abstract: According to an extensive evaluation of published studies, there is a shortage of research on systematic literature reviews related to machine learning prediction techniques and methodologies in soil improvement using green materials. A literature review suggests that machine learning algorithms are effective at predicting various soil characteristics, including compressive strength, deformations, bearing capacity, California bearing ratio, compaction performance, stress–strain behavior, geotextile pullout strength behavior, and soil classification. The current study aims to comprehensively evaluate recent breakthroughs in machine learning algorithms for soil improvement using a systematic procedure known as PRISMA and meta-analysis. Relevant databases, including Web of Science, ScienceDirect, IEEE, and SCOPUS, were utilized, and the chosen papers were categorized based on: the approach and method employed, year of publication, authors, journals and conferences, research goals, findings and results, and solution and modeling. The review results will advance the understanding of civil and geotechnical designers and practitioners in integrating data for most geotechnical engineering problems. Additionally, the approaches covered in this research will assist geotechnical practitioners in understanding the strengths and weaknesses of artificial intelligence algorithms compared to other traditional mathematical modeling techniques.

Keywords: PRISMA; soil improvement; by-product; artificial intelligence; green materials; environmental impact (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
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